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Regression Based Bandwidth Selection for Segmentation Using Parzen Windows
Separating Material from Shape and Illumination 1387
Learning a Classification Model for Segmentation
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AdaBoost algorithm analysis applied approach approximation background boundary calibration camera classification cluster color color histogram components Computer Vision constraint contour corresponding curve database defined density detection diffuse disparity distance measure distribution dynamic dynamic texture edge eigenvectors equation error estimation example Figure filter frame function Gaussian geometry given global graph histogram IEEE IEEE Trans inpainting input image iteration Kalman filter kernel labeled learning likelihood linear matching matrix method minimal motion normal object observation obtained occlusion optical flow optimal outliers parameters Pattern Recognition performance pixels plane points position problem Proc projection proposed random RANSAC reconstruction region representation retrieval robust rotation sample scene scheme segmentation selection sequence shape shown shows spatial specular specular reflection statistical stereo structure Support Vector Machines surface surface normal techniques tensor textons texture tion tracking transformation update vector visual voxel Weibull distribution